Research on Seismic Vulnerability Analysis Method for Bridges Based on Modification of Modal Parameters

LI Ningbo; ZHU Qingshuai; ZHOU Yu; SHU Qingdong; CHENG Cai

doi:10.13409/j.cnki.jdpme.20231212006

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Research on Seismic Vulnerability Analysis Method for Bridges Based on Modification of Modal Parameters

更新时间：2025-05-07

- Research on Seismic Vulnerability Analysis Method for Bridges Based on Modification of Modal Parameters
- Journal of Disaster Prevention and Mitigation Engineering Vol. 45, Issue 2, Pages: 384-392(2025)
- 作者机构：
  
  1.安徽建筑大学土木工程学院，安徽合肥 230601
  2.建筑健康监测与灾害预防技术国家地方联合工程实验室，安徽合肥 230601
  3.苏交科集团有限公司，江苏南京 210000
- 作者简介：
- 基金信息：
- DOI：10.13409/j.cnki.jdpme.20231212006
  CLC： TU352.1
- Received：12 December 2023，
  
  Revised：2024-04-16，
  
  Published：28 April 2025
- 稿件说明：
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李宁波,朱清帅,周宇等.基于模态参数修正的桥梁地震易损性分析方法研究[J].防灾减灾工程学报,2025,45(02):384-392.

LI Ningbo,ZHU Qingshuai,ZHOU Yu,et al.Research on Seismic Vulnerability Analysis Method for Bridges Based on Modification of Modal Parameters[J].Journal of Disaster Prevention and Mitigation Engineering,2025,45(02):384-392.
李宁波,朱清帅,周宇等.基于模态参数修正的桥梁地震易损性分析方法研究[J].防灾减灾工程学报,2025,45(02):384-392. DOI： 10.13409/j.cnki.jdpme.20231212006.

LI Ningbo,ZHU Qingshuai,ZHOU Yu,et al.Research on Seismic Vulnerability Analysis Method for Bridges Based on Modification of Modal Parameters[J].Journal of Disaster Prevention and Mitigation Engineering,2025,45(02):384-392. DOI： 10.13409/j.cnki.jdpme.20231212006.

摘要

为深入研究在役桥梁的地震易损性，考虑到有限元软件建模过程中系统参数不确定问题，基于BP神经网络修正了的桥梁模型。以我国华东地区某变截面连续梁桥为例，依托Midas Civil软件进行精细化建模，以桥梁的动力响应作为输入，以结构参数作为输出，根据桥梁实测模态对初始有限元模型进行修正，修正结果表明BP神经网络能使模型误差由22.92%降低到4.58%，提高计算精度。结合《公路桥梁抗震设计规范》中桥梁隔震设计理论更换铅芯橡胶支座，采用增量动力法（IDA）分别对初始模型修正前、修正后、隔震优化后三者进行非线性时程分析，分别提取桥梁在不同地震波下的结构响应并绘制易损性曲线。数据结果表明，修正后模型的损伤概率略微小于修正前，采用隔震支座能有效降低桥梁结构在地震荷载作用下破坏的概率，最高损伤概率可降低约42%，隔震效果明显。

Abstract

To investigate the seismic vulnerability of in-service bridges

considering the uncertainty of system parameters in finite element modeling

this study presents a bridge model modified by a backpropagation (BP) neural network. A variable cross-section continuous girder bridge in East China was taken as an example. Utilizing Midas Civil software

a refined finite element model was established

where the bridge's dynamic responses served as inputs and structural parameters as outputs. The original finite element model was modified using the measured modal data of the bridge. The modification results showed that the BP neural network reduced model error from 22.92% to 4.58%

enhancing computational accuracy. Following the seismic isolation design theory in China's "Code for Seismic Design of Highway Bridges"

lead rubber bearings were installed. The incremental dynamic analysis (IDA) method was employed to conduct nonlinear time-history analyses on three models: original model

modified model

and isolation-optimized model. Structural responses under different seismic waves were extracted to develop vulnerability curves. The data results indicated that the modified model had slightly lower damage probability than the original one. The use of seismic isolation bearings effectively reduced the probability of structural failure under seismic load

with the maximum damage probability decreasing by approximately 42%

demonstrating significant isolation effectiveness.

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references

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